My daughter is now looking at colleges. These courses both from MIT and Stanford give me, the one who will be paying, a sense of what my 70K per year will be buying. I think this is great marketing for less cost than those mass mailings all the schools seem to be cramming into our mailbox. I just wish all these tier 1 schools would offer something online, it would make judging the quality of what I will soon be buying a lot easier.
I just have a small quibble about the statement "3 years to be exact". Having built several companies over the years, no time line is exact. That doesn't mean it's not a good estimate, just that the cliche may be taken literally.
However, how does this fit into the "fail often, fail quickly" advice I have seen so often in this forum? Does quickly mean 3 years? Just a thought...
Fail often, fail quickly mostly applies to the failures you will have along the way during those 3 years. I am not sure it will take 3 years to break even, but the business taking a few years time to break even is definitely not in contradiction with the fail often fail quickly advice.
In fact, fail often fail quickly, if done effectively, will lessen the number of years to break even (IMO).
> Three years. Sometimes a bit less, sometimes a bit more.
3 years is pretty quick in terms of success, in terms of failure it is an eternity. It all depends on what you perceive the trend to be. If the trend 'flatlines' then you can draw a negative conclusion much earlier. After a year or less you can probably predict quite accurately what the future will look like.
Three years is pretty quick on the scale of a viable business.
In the early days, it is an eternity in terms of strategy when that strategy is failing.
In other words, three years may be an appropriate timeline in which to view overall sunk costs, but that doesn't mean that the business shouldn't be prepared to pivot several times within that period.
great combination of electrical engineering, core physics (classical and if you want it - GPS does touch SR and GR), programming (at app and VSDL levels). Such projects should be a regular part of lab curriculum for any engineering student (including programmers).
I knew a doctor at a noted research hospital who was using Bayes to fine tune cancer treatments. I still wonder if he was on the right track with his research or was he as you put it a "crank".
I think you're misunderstanding, there is nothing about using Bayesian analysis that makes you a crank. Using Bayesian analysis where it makes sense is good and sensible and standard practice among just about everybody in the field (although there are some argument about the size an shape of the set where Bayesian analysis makes sense). Cancer treatment would be a perfect example of an area where Bayesian analysis makes sense.
However, for some unknown reason, Bayesian analysis has also become a trendy buzzwords among huge number of crazy internet trolls who seem to think it's a magic formula that can solve all problems, and who have some paranoid delusions that "they" are trying to suppress the knowledge of Bayesian analysis.
On the other hand, there's not a lot of Bayesian analysis taught in high school, statistics 101, or any of the other places that non-stats-nerds are likely to be. It's useful for a lot of things, easy and intuitive (thus its popularity amongst statistical laymen). So why isn't it taught?
The costs of teaching and learning Bayesian Analysis are low (it's just not as hard as, say, the method of moments), and it does have benefits.
My old stats 201 book (Wackerly, Mendenhall and Scheaffer) covers Probability, Discrete Random Variables, Continuous Random Variable, Multivariate Distributions, Functions of RVs, The Central Limit Theorem, Estimation, Properties of Point estimators and Methods of Estimation, Hypothesis Testing, Linear Models / Least Squares, Designing Experiments, Categorical Data, and Nonparametric Statistics. 15 topics (including the introduction), and Bayesian analysis isn't mentioned. Bayes Law is (of course), but only as a theoretical tool, and for solving toy problems about pirates, beads, and rats in the second chapter.
You wouldn't take an engineering analysis book seriously if it didn't mention FEA, but statistics courses can hold their heads up while completely ignoring a useful and easy to teach tool.
Of course, good statisticians and mathematicians will learn about it (later, or on their own), but there's leagues of economists and engineers coming out who will never bother wrapping their heads around it.
Of course, it's entirely possible that it's not so much a conspiracy spearheaded by old-guard frequentists so much as introductory stats courses being focused on teaching a core of theory (LS and MoM), rather then teaching practical tools to people who will use them. You could also accuse introductory math courses of ignoring useful, fun, and easy stuff (scaling?), while focusing on an old, predefined, widely accepted body of theory.
I find that most of the Bayesian 'militants', if you will, aren't actually doing research using Bayesian methods, but are more recent converts writing blog posts about it. Most researchers I know who use Bayesian methods in research aren't even particularly dogmatic about it, and don't go around writing essays about the Evil Frequentists and Oppression of Bayesianism.
In fact, most (at least in my circle) use frequentist methods as well, as well as methods that don't fall comfortably into either camp. In ML it's particularly common for the same researcher to use any/all of kernel density estimation, Bayesian graphical models, SVMs, etc., depending on the problem.
